SpiNNaker: Enhanced multicast routing
SpiNNaker: Enhanced multicast routing
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DOI：
10.1016/j.parco.2015.01.002
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发表时间：
2015-06-01
影响因子：
1.4
通讯作者：
Furber, Steve B.
中科院分区：
文献类型：
--
作者：
Navaridas, Javier;Lujan, Mikel;Furber, Steve B.
人脑是一个复杂的生物神经网络，其特点是神经元之间高度连通。任何被设计来模拟大规模尖峰神经元网络的系统都需要以尖峰事件的形式支持这种连接和相关的通信流量。本文研究了如何为Spinnaker生成多播路由，Spinnaker是一种专门构建的低功耗大规模并行体系结构。所讨论的算法是Spinnaker高效运行的重要组成部分，因为生成多播路由是已知的NP-完全问题。事实上，在文献中已经对多播通信进行了广泛的研究，但我们发现没有适用于Spinnaker的现有算法。该算法充分利用了二维三角环面拓扑的规律性和硬件级选择组播的有效性。在考虑从最坏情况到实际神经应用的不同目的地分布的情况下，对算法的参数及其有效性进行了综合研究。结果表明，提出的两种新算法可以显著降低对互连基础设施的压力，同时仍能有效地在生产环境中使用。(C)2015年提交人。爱思唯尔出版公司(Elsevier B.V.)
The human brain is a complex biological neural network characterised by high degrees of connectivity among neurons. Any system designed to simulate large-scale spiking neuronal networks needs to support such connectivity and the associated communication traffic in the form of spike events. This paper investigates how best to generate multicast routes for SpiNNaker, a purpose-built, low-power, massively-parallel architecture. The discussed algorithms are an essential ingredient for the efficient operation of SpiNNaker since generating multicast routes is known to be an NP-complete problem. In fact, multicast communications have been extensively studied in the literature, but we found no existing algorithm adaptable to SpiNNaker. The proposed algorithms exploit the regularity of the two-dimensional triangular torus topology and the availability of selective multicast at hardware level. A comprehensive study of the parameters of the algorithms and their effectiveness is carried out in this paper considering different destination distributions ranging from worst-case to a real neural application. The results show that two novel proposed algorithms can reduce significantly the pressure exerted onto the interconnection infrastructure while remaining effective to be used in a production environment. (C) 2015 The Authors. Published by Elsevier B.V.